Several well-known word embedding are provided to extract the vector representation of the input tweets with aims to capture the semantic features for each word and the relationship among them. Word2Vec has been provided by mikolov2013distributed, Glove [1]}, AraVec aravec and the recent contextual embedding ElMo by [2]} and BERT devlin2018bert. In this research, we used AraVec, Word2Vec and the pre-trained BERT embedding to trained the performed model. It is a language representation model and becoming the state of art model for the most of NLP research.
